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501
Designing Cellular Mobile Networks Using Non{Deterministic Iterative Heuristics
Published 2020“…Hence, a randomized, heuristic algorithm, such as Simulated Evolution is used in this work to optimize the transmission costs in cellular networks. …”
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502
A method for data path synthesis using neural networks
Published 2017“…A sequential simulator was implemented for the proposed algorithm on a Linux Pentium PC under X Windows. Several circuits hare been attempted, all yielding sub-optimal solutions.…”
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conferenceObject -
503
AI-Augmented Metasurface Synthesis for Dynamic Beam Steering in Reconfigurable Antenna Arrays
Published 2025“…As compared to the conventional heuristic methods, for example, genetic algorithms (GA), particle swarm optimization (PSO), the approach based on DRLs has rapid policy convergence, has relatively less computational latency, and is autonomous to adapt to dynamic wireless conditions. …”
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504
An Evolutionary Meta-Heuristic for State Justification in Sequential Automatic Test Pattern Generation
Published 2001“…Evolutionary algorithms have been effective in solving many search and optimization problems. …”
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505
Shuffled Linear Regression with Erroneous Observations
Published 2019“…We propose an optimal recursive algorithm that updates the estimate from the underdetermined function that is based on that permutation-invariant constraint. …”
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conferenceObject -
506
Optimum Track to Track Fusion Using CMA-ES and LSTM Techniques
Published 2024“…The first method uses an offline technique based on a global optimizer called the CMA-ES algorithm and the second one uses LSTM in its different forms to learn the online adjustment of the fusion weights between the two tracks. …”
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507
Just-in-time defect prediction for mobile applications: using shallow or deep learning?
Published 2023“…Experimental results demonstrated that DL algorithms leveraging sampling methods perform significantly worse than the decision tree-based ensemble method. …”
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508
Multi Agent Reinforcement Learning Approach for Autonomous Fleet Management
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doctoralThesis -
509
Security in wire/wireless networks: sniffing attacks prevention/detection techniques in LAN networks & the effect on biometric technology
Published 2010“…In our research, we evaluated the most famous security solutions and classifying them according to their efficiency against detecting or preventing the types of Address Resolution Protocol [ARP] Spoofing attacks. Based on the surprising experimental results done by a previous study in the security lab which proposed an optimal algorithm to enhance their ability against the two famous network attacks; we implemented the proposed algorithm by this study and stimulate the experiment in order to test the algorithm performance. …”
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510
Estimation of power grid topology parameters through pilot signals
Published 2016“…In this context, a method to estimate the connection status of distributed generators and the system topology is proposed in this paper, the goal being to obtain up to date information on the power system network’s configuration. …”
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conferenceObject -
511
Nested ensemble selection: An effective hybrid feature selection method
Published 2023“…Numerical experiments on synthetic and real-life data demonstrate the effectiveness of the proposed method. The NES algorithm achieves perfect precision on the synthetic data and near optimal accuracy on the real-life data. …”
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512
Enhancement of blocking performance in all-optical WDM networks via wavelength reassignment and route deviation
Published 2012“…The performance of the rerouting strategy is studied through extensive simulations in the context of networks employing the least congested path (LCP) routing algorithm and the first-fit (FF) wavelength assignment strategy. …”
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513
Design and development of an embedded controller for roboticmanipulator
Published 1998“…Mohseni's Proposed Algorithm, MPA, has been incorporated into the embedded controller to reduce the computational efforts and to obtain a close-to-optimal control law. …”
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514
FoGMatch
Published 2019“…Our solution consists of (1) two optimization problems, one for the IoT devices and one for the fog nodes, (2) preference functions for both the IoT and fog layers to help them rank each other on the basis of several criteria such latency and resource utilization, and (3) centralized and distributed intelligent scheduling algorithms that consider the preferences of both the fog and IoT layers to improve the performance of the overall IoT ecosystem. …”
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masterThesis -
515
Multidimensional Gains for Stochastic Approximation
Published 2019“…The two algorithms assume full knowledge of the Jacobian. The recursive algorithms are proposed for generating the optimal iterative-dependent matrix gain. …”
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516
Performance driven standard-cell placement using the geneticalgorithm
Published 1995“…In this paper we present a timing-driven placer for standard-cell IC design. The placement algorithm follows the genetic paradigm. Besides optimizing for area and wire length, the placer minimizes the propagation delays on a predicted set of critical paths. …”
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517
An evolutionary meta-heuristic for state justification insequential automatic test pattern generation
Published 2001“…Evolutionary algorithms have been effective in solving many search and optimization problems. …”
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518
Iterative heuristics for multiobjective VLSI standard cellplacement
Published 2001“…We employ two iterative heuristics for the optimization of VLSI standard cell placement. These heuristics are based on genetic algorithms (GA) and tabu search (TS) respectively. …”
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519
The bus sightseeing problem
Published 2023“…A mixed-integer programming formulation for the BSP is provided and solved by a Benders decomposition algorithm. For large-scale instances, an iterated local search based metaheuristic algorithm is developed with some tailored neighborhood operators. …”
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520
Traffic Offloading with Channel Allocation in Cache-Enabled Ultra-Dense Wireless Networks
Published 2018“…We also propose efficient sub-optimal hierarchical tree-based algorithms that operate in real time with dynamic and fast solutions for ultra dense networks. …”
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